It is common in high-performance computing (HPC) systems and other information processing systems for multiple compute nodes to access a cluster file system. For example, HPC systems such as supercomputers typically include large numbers of compute nodes that access a parallel file system, distributed file system or other type of cluster file system. A cluster file system as the term is broadly used herein generally allows multiple compute nodes or other types of clients to share access to files over a network.
One well-known cluster file system is the Lustre file system. Lustre is a Linux-based high performance cluster file system utilized for computer clusters ranging in size from small workgroup clusters to large-scale, multi-site clusters. Lustre can readily scale to support tens of thousands of clients, petabytes of storage capacity, and hundreds of gigabytes per second of aggregate input-output (IO) throughput. Due to its high performance and scalability, Lustre is utilized in many supercomputers, as well as other complex computing environments, including large enterprise data centers.
In conventional Lustre implementations, it can be difficult to balance the conflicting requirements of storage capacity and IO throughput. IO operations on object storage servers are generally performed directly with back-end storage arrays associated with those servers, and the corresponding storage devices may not be well matched to the current needs of the system. This can lead to situations in which either performance is less than optimal or the costs of implementing the system become excessive.
For example, certain types of highly cost effective storage, such as scale-out network attached storage, are often seen as failing to provide performance characteristics that are adequate for use with supercomputers and other complex computing environments that utilize Lustre file systems.
Also, it can be difficult to integrate Lustre file systems and other types of cluster file systems with analytics platforms, such as analytics platforms utilized to implement “Big Data” analytics functionality involving complex data sources.